Career Path For Data Scientist

Analytics is an industry that is all set for exponential growth. With the deluge of data, there is an urgent drive to make smarter and quickerdata-based decisions by organizations, who are looking to have a competitive advantage. This has resulted in a huge demand for professionals skilled in Analytics. With organizations looking for ways to capitalize the power of Big Data, the demand for such professionals increases exponentially. The number of Analytics-related job postings in Indeed, Dice, Glassdoor, and other job portals are seeing a substantial increase compared to the previous year.

Looking at the above job trend of Big Data Analytics, it is obvious that there is a huge demand for this skill andindicates a rampant growth with no signs of slowing down. Analytics has already started to transform businesses as they are helping organizations to make smarter decisions, making Analytics the new career of choice for those who want to stay ahead.

So, there is no doubt about going forward with a career in Analytics.

Career in Analytics

Becoming an Analytics professional is an excellent career choice for those who want a challenging and rewarding career in Data Analytics and Business Intelligence. It is also a great career choice for those who enjoy not only exploring data on the surface but also loves touncover the story behind the data.

When thinking of starting a career in Data Analytics you come across questions like, ‘Where to start?’, ‘What to learn?’, ‘Which tool to learn?’ and ‘Where to learn?’

Let’s begin with the basic requirements for starting a Data Analytics career.

How to start your career in Data Analytics?

To begin with, you need to be a graduate in Mathematics, Statistics or Computer Science, which are the three core areas of analytic roles. Besides this, the following are the essential skills required to get into the field of analytics.

Analytics Career Path

Here is the suggested career path for Data Analytics professionals:

Why this career path?

One might ask why they should follow this career path. The answer is quite simple. If your ultimate aim is work on Machine Learning, you need to learn the basics first. This includes R programming. With a sound foundation in R, you can delve deeper into the advanced concepts of Machine Learning. Hence, this is the recommended career direction.

Brief Introduction to R Programming

R is a programming language and software environment for statistical computing and graphics. The R language is widely used by Statisticians and Data Miners for data analysis. R allows you to visualize data, run statistical tests, and apply Machine Learning algorithms. It gives you access to front-line technology and is a vital asset to those who value the importance of Analytics and learning R is advisable to help you boost your career.

What is Business Analytics?

Business Analytics is all about gaining insights from data and using statistics to make data-driven decisions. This is done by combining various skills, technologies, and best practices to make these decisions. Business Analytics can be implemented for doing the following:

Effective use of transactional data.

Coming up with a strategy by understanding what works, what doesn’t and what is needed.

Predict outcomes based on previous data.

Who needs R Programming?

R Programming is for anyone who is passionate about starting their career in Analytics. It is a prerequisite for learning Machine Learning, as it covers the basics of what is required to learn the concepts in Machine Learning. A basic knowledge in Statistics will prove to be beneficial for learning R as it usually delves deep into the concepts of Statistics.

I work on Excel for performing statistical analysis; can I boost my career with R?

Excel is used for simple statistical analysis. For data analysis beyond basic statistics, R is required. R surpasses Excel for many reasons. Some of them are as follows:

Access to thousands of libraries to do just about anything

Ability to write your own functions and libraries

Control flow

Plotting (More variety and control in R)

Open source

Transferrable programs

With R in your skillset, you can perform functions beyond the normal statistical analysis.

I want to become a Data Analysts, so what should I learn to become one?

You need to be proficient in the following concepts to become a Data Analyst:

Concepts of Statistics

Programming for the statistics; Scripting languages(R language )

Learn Machine Learning concepts like:

Classification

Clustering

Collaborative filtering

Knowledge in SQL is beneficial

What will I Learn from the R Programming course?

This Business Analytics with R course focuses on teaching about the need for Data Analysis and concentrates on Statistics and Regression techniques. The following is the overview of what the course covers:

Brief Introduction to Business Analytics

In-depth understanding of R Programing

Data manipulation in R

Data visualization in R

In-depth understanding of Statistics

Analysis of variance (ANOVA) and hypothesis testing

Regression techniques (focused more on linear regression)

Why do you need to learn Machine Learning?

Machine Learning is the science of getting computers to act without being explicitly programmed. Inrecent years, Machine Learning has resulted in self-driving cars, practical speech recognition, effective web search, and has vastly enrichedthe understanding of the human genome. Machine learning is so prevalent today that you probably use it dozens of times in a day without even knowing.

In this course, you will learn about the most effective Machine Learning techniques, and gain experience from implementing them. More importantly, you’ll not only learn about the theoretical foundations of Machine Learning but also gain the practical know-how needed to efficiently and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to Machine Learning and AI (Artificial Intelligence).

This course provides a broad introduction to Machine Learning, Data Mining, and Statistical Pattern Recognition. The topics include are:

During the course, you will also implement numerous case studies and applications, so that you’ll know how to apply learning algorithms.

Who should go for Machine Learning?

Grad students, post grad and Ph.D scholars (from statistical background), entry-level software professionals, and anyone who is interested in a data analytics career. Knowledge in Business Analytics with Ris a prerequisite for taking up Machine Learning course.

I’m a Big Data Developer, can I take up Machine Learning course?

Absolutely! In fact, this will be the right choice.

The job of a Big Data Developer is to extract data relevant for analysis. After extracting the relevant data, the next course of action is to create the models fromthe extracted data using Machine Learning algorithms.

So, having knowledge in Machine Learning will enhance the chances of getting a good job.

I’m interested in Predictive Modeling, how will this course help me?

Predictive Modeling is a process used in predictive analytics to create a statistical model of future behavior. For one to learn predictive modelling, understanding the regression and classification techniques is a must.

Regression deals with statistical methodologies. Our ‘Business analytics with R’course covers variousRegression techniques like linear regression, multiple regression, and logistic regression. It also covers essential statistics, which is a must for learning the above mentioned algorithms.

Classification is a type of Predictive modeling algorithms which classifies the data, based on historical data.

To understand the classification technique, the necessary machine learning algorithms is covered in our ‘Machine learning with R’course.

I’m a Data Mining professional, what topics in this course will be relevant to me?

Data mining or knowledge discovery is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.

We cover data mining algorithms like:

Nearest Neighbor Classification

Naïve Bayes Classification

Decision trees

Random Forest

Support vector machines

Artificial Neural Networks

What topics are covered in the Machine Learning course?

This course covers Statistics as well, but not to the extent of the Business Analytics with R course. It includes the following topics:

Brief Introduction to Machine Learning

Unsupervised and supervised Machine Learning techniques

Classification algorithms

Nearest Neighbor Classification

Naïve Bayes Classification

Decision trees

Random Forest

Support vector machines

Artificial Neural Networks

Regression models

Clustering analysis (Kmeans, hierarchical clustering)

Principal component analysis (PCA)

Forecasting Principles

I’m a fresher, where do I start?

Entry-level software professionals and fresher should focus on knowing the basics of Machine Learning, which includes R programming, which is covered extensively in the Business Analytics with R course. Then only you can proceed to learn the advanced concepts in Machine Learning.

Conclusion

Big Data Analytics is in the frontiers of IT. But, in spite of it being a ‘Hot’ job, there is still a large number of unfilled jobs due to the shortage of talent. For professionals, who are skilled in Big Data Analytics, there are abundant opportunities out there.This is the right time to update yourself with the necessary skills to have the much-required edge in your Analytics career and those who invest in it will be on the fast track to success.

Need further clarifications? Feel free to mail us at [email protected] .You can also ask your questions in the comments section below.

One Comment

Great explanations regarding the career in data scientist. There is a lot of scope in data science field. I think if you are dealing with data analytics, it is not any trend that is here for a while. It is in fact what the future will become. If you are opting for an analytics training to try, then it may not be a good deal for you. Thanks.